DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_4:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
# from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
# offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [2]:
project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')
project_data.head(2)
Out[2]:
In [3]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
In [4]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[4]:
1.2 Data Analysis¶
In [5]:
# PROVIDE CITATIONS TO YOUR CODE IF YOU TAKE IT FROM ANOTHER WEBSITE.
# https://matplotlib.org/gallery/pie_and_polar_charts/pie_and_donut_labels.html#sphx-glr-gallery-pie-and-polar-charts-pie-and-donut-labels-py
y_value_counts = project_data['project_is_approved'].value_counts()
print("Number of projects thar are approved for funding ", y_value_counts[1], ", (", (y_value_counts[1]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
print("Number of projects thar are not approved for funding ", y_value_counts[0], ", (", (y_value_counts[0]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
recipe = ["Accepted", "Not Accepted"]
data = [y_value_counts[1], y_value_counts[0]]
wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle="-"),
bbox=bbox_props, zorder=0, va="center")
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
ax.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment, **kw)
ax.set_title("Nmber of projects that are Accepted and not accepted")
plt.show()
SUMMARY: There are total 109248 applied projects,out of which 84.85% are approved for funding and 15.14% are not approved.
1.2.1 Univariate Analysis: School State¶
In [6]:
# Pandas dataframe groupby count, mean: https://stackoverflow.com/a/19385591/4084039
temp = pd.DataFrame(project_data.groupby("school_state")["project_is_approved"].apply(np.mean)).reset_index()
# if you have data which contain only 0 and 1, then the mean = percentage (think about it)
temp.columns = ['state_code', 'num_proposals']
# How to plot US state heatmap: https://datascience.stackexchange.com/a/9620
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
data = [ dict(
type='choropleth',
colorscale = scl,
autocolorscale = False,
locations = temp['state_code'],
z = temp['num_proposals'].astype(float),
locationmode = 'USA-states',
text = temp['state_code'],
marker = dict(line = dict (color = 'rgb(255,255,255)',width = 2)),
colorbar = dict(title = "% of pro")
) ]
layout = dict(
title = 'Project Proposals % of Acceptance Rate by US States',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)',
),
)
fig = go.Figure(data=data, layout=layout)
offline.iplot(fig, filename='us-map-heat-map')
In [7]:
# https://www.csi.cuny.edu/sites/default/files/pdf/administration/ops/2letterstabbrev.pdf
temp.sort_values(by=['num_proposals'], inplace=True)
print("States with lowest % approvals")
print(temp.head(5))
print('='*50)
print("States with highest % approvals")
print(temp.tail(5))
In [8]:
#stacked bar plots matplotlib: https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html
def stack_plot(data, xtick, col2='project_is_approved', col3='total'):
ind = np.arange(data.shape[0])
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, data[col3].values)
p2 = plt.bar(ind, data[col2].values)
plt.ylabel('Projects')
plt.title('Number of projects aproved vs rejected')
plt.xticks(ind, list(data[xtick].values))
plt.legend((p1[0], p2[0]), ('total', 'accepted'))
plt.show()
In [9]:
def univariate_barplots(data, col1, col2='project_is_approved', top=False):
# Count number of zeros in dataframe python: https://stackoverflow.com/a/51540521/4084039
temp = pd.DataFrame(project_data.groupby(col1)[col2].agg(lambda x: x.eq(1).sum())).reset_index()
# Pandas dataframe grouby count: https://stackoverflow.com/a/19385591/4084039
temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']
temp.sort_values(by=['total'],inplace=True, ascending=False)
if top:
temp = temp[0:top]
stack_plot(temp, xtick=col1, col2=col2, col3='total')
print(temp.head(5))
print("="*50)
print(temp.tail(5))
In [10]:
univariate_barplots(project_data, 'school_state', 'project_is_approved', False)
SUMMARY: Every state has greater than 80% success rate in approval
1.2.2 Univariate Analysis: teacher_prefix¶
In [11]:
univariate_barplots(project_data, 'teacher_prefix', 'project_is_approved' , top=False)
SUMMARY : The teacher with prefix 'Mrs.' have highest approval percentage while with prifix 'Dr' have lowest.
1.2.3 Univariate Analysis: project_grade_category¶
In [12]:
univariate_barplots(project_data, 'project_grade_category', 'project_is_approved', top=False)
SUMMARY : The Project Grade Category PreK-2 has highest number of applications.The Project Grade Category 3-5 has highest project approval percentage.
1.2.4 Univariate Analysis: project_subject_categories¶
In [13]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
In [14]:
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
project_data.head(2)
Out[14]:
In [15]:
univariate_barplots(project_data, 'clean_categories', 'project_is_approved', top=20)
SUMMARY : The Project subject category Literacy_Language has highest number of applications while subject category Warmth Care_Hunger has highest project approval percentage.
In [16]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
In [17]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved category wise')
plt.xticks(ind, list(sorted_cat_dict.keys()))
plt.show()
In [18]:
for i, j in sorted_cat_dict.items():
print("{:20} :{:10}".format(i,j))
SUMMARY : There is lot of variability in number of project we have per cleaned category.The Project Grade Category Literacy_Language has highet project approvals.
In [19]:
project_data.columns
Out[19]:
1.2.5 Univariate Analysis: project_subject_subcategories¶
In [20]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
In [21]:
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
project_data.head(2)
Out[21]:
In [22]:
univariate_barplots(project_data, 'clean_subcategories', 'project_is_approved', top=50)
In [23]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
In [24]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_sub_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_sub_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved state wise')
plt.xticks(ind, list(sorted_sub_cat_dict.keys()))
plt.show()
In [25]:
for i, j in sorted_sub_cat_dict.items():
print("{:20} :{:10}".format(i,j))
SUMMARY : The Project Grade Category Literacy has highet project as well as Project approvals.Ther are huge variability in occurance of each of these sub_categories also.
1.2.6 Univariate Analysis: Text features (Title)¶
In [26]:
#How to calculate number of words in a string in DataFrame: https://stackoverflow.com/a/37483537/4084039
word_count = project_data['project_title'].str.split().apply(len).value_counts()
word_dict = dict(word_count)
word_dict = dict(sorted(word_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(word_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(word_dict.values()))
plt.ylabel('Numeber of projects')
plt.xlabel('Numeber words in project title')
plt.title('Words for each title of the project')
plt.xticks(ind, list(word_dict.keys()))
plt.show()
SUMMARY : Most of the projects contain Title with 4 or 5 words.
In [27]:
approved_title_word_count = project_data[project_data['project_is_approved']==1]['project_title'].str.split().apply(len)
approved_title_word_count = approved_title_word_count.values
rejected_title_word_count = project_data[project_data['project_is_approved']==0]['project_title'].str.split().apply(len)
rejected_title_word_count = rejected_title_word_count.values
In [28]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_title_word_count, rejected_title_word_count])
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project title')
plt.grid()
plt.show()
In [29]:
plt.figure(figsize=(10,3))
sns.kdeplot(approved_title_word_count,label="Approved Projects", bw=0.6)
sns.kdeplot(rejected_title_word_count,label="Not Approved Projects", bw=0.6)
plt.legend()
plt.show()
SUMMARY : The Distribution plot of approved project is slightly more than Rejected project Distribution.Which means the number of words in project title for approved project is more than 4 as compare to rejected project.
1.2.7 Univariate Analysis: Text features (Project Essay's)¶
In [30]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [31]:
approved_word_count = project_data[project_data['project_is_approved']==1]['essay'].str.split().apply(len)
approved_word_count = approved_word_count.values
rejected_word_count = project_data[project_data['project_is_approved']==0]['essay'].str.split().apply(len)
rejected_word_count = rejected_word_count.values
In [32]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_word_count, rejected_word_count])
plt.title('Words for each essay of the project')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project essays')
plt.grid()
plt.show()
In [33]:
plt.figure(figsize=(10,3))
sns.distplot(approved_word_count, hist=False, label="Approved Projects")
sns.distplot(rejected_word_count, hist=False, label="Not Approved Projects")
plt.title('Words for each essay of the project')
plt.xlabel('Number of words in each eassay')
plt.legend()
plt.show()
SUMMARY : The Project Essay with more than 260 words have higher chances of approval.
1.2.8 Univariate Analysis: Cost per project¶
In [34]:
# we get the cost of the project using resource.csv file
resource_data.head(2)
Out[34]:
In [35]:
# https://stackoverflow.com/questions/22407798/how-to-reset-a-dataframes-indexes-for-all-groups-in-one-step
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
price_data.head(2)
Out[35]:
In [36]:
# join two dataframes in python:
project_data = pd.merge(project_data, price_data, on='id', how='left')
In [37]:
project_data.columns
Out[37]:
In [38]:
approved_price = project_data[project_data['project_is_approved']==1]['price'].values
rejected_price = project_data[project_data['project_is_approved']==0]['price'].values
In [39]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_price, rejected_price])
plt.title('Box Plots of Cost per approved and not approved Projects')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Price')
plt.grid()
plt.show()
In [40]:
plt.figure(figsize=(10,3))
sns.distplot(approved_price, hist=False, label="Approved Projects")
sns.distplot(rejected_price, hist=False, label="Not Approved Projects")
plt.title('Cost per approved and not approved Projects')
plt.xlabel('Cost of a project')
plt.legend()
plt.show()
In [41]:
# http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable
x = PrettyTable()
x.field_names = ["Percentile", "Approved Projects", "Not Approved Projects"]
for i in range(0,101,5):
x.add_row([i,np.round(np.percentile(approved_price,i), 3), np.round(np.percentile(rejected_price,i), 3)])
print(x)
SUMMARY : Chance of approval is varing with respect to cost, if the Cost per Project is lesser then chances of approval are greater.
1.2.9 Univariate Analysis: teacher_number_of_previously_posted_projects
In [42]:
tnp = pd.DataFrame(project_data,columns=['teacher_number_of_previously_posted_projects','project_is_approved'])
In [43]:
approved = tnp[tnp['project_is_approved']==1]['teacher_number_of_previously_posted_projects'].values
not_approved = tnp[tnp['project_is_approved']==0]['teacher_number_of_previously_posted_projects'].values
In [44]:
plt.figure(figsize=(10,6))
sns.distplot(approved, hist=False, label="Approved Projects")
sns.distplot(not_approved, hist=False, label="Not Approved Projects")
plt.title('teacher_number_of_previously_posted_projects')
plt.legend()
plt.show()
In [45]:
# http://zetcode.com/python/prettytable/
x = PrettyTable()
x.field_names = ["Percentile", "Approved Projects", "Not Approved Projects"]
for i in range(0,101,5):
x.add_row([i,np.round(np.percentile(approved,i), 3), np.round(np.percentile(not_approved,i), 3)])
print(x)
In [46]:
univariate_barplots(project_data, 'teacher_number_of_previously_posted_projects', 'project_is_approved', top=50)
In [47]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_word_count, rejected_word_count])
plt.title('teacher_number_of_previously_posted_projects')
plt.xticks([1,2],('Approved Projects','Not Approved Projects'))
plt.ylabel('number_of_previously_posted_projects')
plt.grid()
plt.show()
In [48]:
t=tnp.groupby(tnp['teacher_number_of_previously_posted_projects'])
p=t.count()
state = tnp.groupby(tnp['teacher_number_of_previously_posted_projects']).sum()
k=state['project_is_approved']/p['project_is_approved']*100
print(k.head(10))
SUMMARY : The distribution of Approved Projects is slightly more than non approved projects.This indicates that Teacher with more previous submissions have better chances of projects getting approved.
1.2.10 Univariate Analysis: project_resource_summary
Please do this on your own based on the data analysis that was done in the above cells
Check if the presence of the numerical digits in the project_resource_summary effects the acceptance of the project or not. If you observe that presence of the numerical digits is helpful in the classification, please include it for further process or you can ignore it.
In [49]:
ps=pd.DataFrame(project_data,columns=['project_resource_summary','project_is_approved'])
In [50]:
ps['project_resource_summary'] = ps['project_resource_summary'].astype(str)
# Identify & Extract digits from the project resource summary
ps['project_resource'] = ps['project_resource_summary'].str.extract('(\d+ )', expand=False).str.strip()
In [51]:
#Fill all NaN values with zeros
ps['project_resource']=ps['project_resource'].fillna(0)
In [52]:
prt=ps.loc[ps['project_is_approved']==1]#positve
prt=prt.loc[prt['project_resource'].astype(int)>0]#non-zero value
size_of_non_zero_positives=prt.shape[0]
size_of_non_zero_positives=float(size_of_non_zero_positives)
print('Total number of non-zero project resource valued positives = '+str(size_of_non_zero_positives))
In [53]:
non_zeros=ps.loc[ps['project_resource'].astype(int)>0]
size_of_non_zeros=non_zeros.shape[0]
size_of_non_zeros=float(size_of_non_zeros)
print('Total number of non-zero project resource valued positives = '+str(size_of_non_zero_positives/size_of_non_zeros*100))
SUMMARY : 90 percent of rows which contains a number are accepted. So if the project resource contains a number which can be describing a the quantity of a thing, then the chances of acceptance are high.
1.3 Text preprocessing¶
1.3.1 Essay Text¶
In [54]:
project_data.head(2)
Out[54]:
In [55]:
# printing some random essays.
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
print(project_data['essay'].values[99999])
print("="*50)
In [56]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [57]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
In [58]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [59]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [60]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
In [61]:
# Combining all the above statemennts
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_essays.append(sent.lower().strip())
In [62]:
# after preprocesing
preprocessed_essays[20000]
Out[62]:
1.3.2 Project title Text
In [63]:
# similarly you can preprocess the titles also
In [64]:
project_data['project_title'].size
Out[64]:
In [65]:
# printing some random titles.
print(project_data['project_title'].values[0])
print("="*50)
print(project_data['project_title'].values[150])
print("="*50)
print(project_data['project_title'].values[1000])
print("="*50)
print(project_data['project_title'].values[20000])
In [66]:
title = decontracted(project_data['project_title'].values[20000])
print(title)
print("="*50)
In [67]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
title = title.replace('\\r', ' ')
title = title.replace('\\"', ' ')
title = title.replace('\\n', ' ')
print(title)
In [68]:
title = re.sub('[^A-Za-z0-9]+', ' ', title)
print(title)
In [69]:
preprocessed_titles = []
# tqdm is for printing the status bar
for sentence in tqdm(project_data['project_title'].values):
sent = decontracted(sentence)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_titles.append(sent.lower().strip())
In [70]:
# after preprocesing
print(preprocessed_titles[3567])
print("="*50)
print(len(preprocessed_titles))
1. 4 Preparing data for models¶
In [71]:
project_data.columns
Out[71]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data
- quantity : numerical
- teacher_number_of_previously_posted_projects : numerical
- price : numerical1.4.1 Vectorizing Categorical data¶
In [72]:
# we use count vectorizer to convert the values into one hot encoded features
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_categories'].values)
print(vectorizer.get_feature_names())
categories_one_hot = vectorizer.transform(project_data['clean_categories'].values)
print("Shape of matrix after one hot encodig ",categories_one_hot.shape)
In [73]:
# we use count vectorizer to convert the values into one hot encoded features
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_subcategories'].values)
print(vectorizer.get_feature_names())
sub_categories_one_hot = vectorizer.transform(project_data['clean_subcategories'].values)
print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)
In [74]:
# Please do the similar feature encoding with state, teacher_prefix and project_grade_category also
In [75]:
# we use count vectorizer to convert the values into one hot encoded features
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
my_counter = Counter()
for word in project_data['school_state'].values:
my_counter.update(word.split())
state_dict = dict(my_counter)
sorted_state_dict = dict(sorted(state_dict.items(), key=lambda kv: kv[1]))
vectorizer = CountVectorizer(vocabulary=list(sorted_state_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['school_state'].values)
print(vectorizer.get_feature_names())
state_one_hot = vectorizer.transform(project_data['school_state'].values)
print("Shape of matrix after one hot encodig ",state_one_hot.shape)
print(type(state_one_hot))
In [76]:
teacher_prefix=[]
for sent in project_data['teacher_prefix'].astype(str).values:
sent = sent.replace('.', '')
sent=sent.replace('nan','Mr')
teacher_prefix.append(sent)
project_data['teacher_prefix']=teacher_prefix
project_data['teacher_prefix']=project_data['teacher_prefix'].fillna('')
my_counter = Counter()
for word in project_data['teacher_prefix'].values:
my_counter.update(word.split())
teacher_dict = dict(my_counter)
sorted_teacher_dict = dict(sorted(teacher_dict.items(), key=lambda kv: kv[1]))
vectorizers = CountVectorizer(vocabulary=list(sorted_teacher_dict.keys()), lowercase=False, binary =True)
vectorizers.fit(project_data['teacher_prefix'].values)
print(vectorizers.get_feature_names())
teacher_one_hot = vectorizers.transform(project_data['teacher_prefix'].values)
print("Shape of matrix after one hot encodig ",teacher_one_hot.shape)
print(type(teacher_one_hot))
In [77]:
#Combining all the above statements
preproc = []
# tqdm is for printing the status bar
for sent in project_data['project_grade_category']:
sent = sent.replace('Grades ', 'Grade_')
sent = sent.replace('-', 'to')
preproc.append(sent)
project_data['project_grade_category']=preproc
In [78]:
my_counter = Counter()
for word in project_data['project_grade_category'].values:
my_counter.update(word.split())
grade_dict = dict(my_counter)
sorted_grade_dict = dict(sorted(grade_dict.items(), key=lambda kv: kv[1]))
vectorizer = CountVectorizer(vocabulary=list(sorted_grade_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['project_grade_category'].values)
print(vectorizer.get_feature_names())
grade_one_hot = vectorizer.transform(project_data['project_grade_category'].values)
print("Shape of matrix after one hot encodig ",grade_one_hot.shape)
print(type(grade_one_hot))
1.4.2 Vectorizing Text data¶
1.4.2.1 Bag of words¶
In [79]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_bow.shape)
1.4.2.2 Bag of Words on `project_title`
In [80]:
vectorizer = CountVectorizer(min_df=10)
title_bow = vectorizer.fit_transform(preprocessed_titles)
print("Shape of matrix after one hot encodig ",title_bow.shape)
print(type(title_bow))
1.4.2.3 TFIDF vectorizer¶
In [81]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
text_tfidf = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_tfidf.shape)
1.4.2.4 TFIDF Vectorizer on `project_title`
In [82]:
vectorizer = TfidfVectorizer(min_df=10)
title_tfidf = vectorizer.fit_transform(preprocessed_titles)
print("Shape of matrix after one hot encodig ",title_tfidf.shape)
print(type(title_tfidf))
1.4.2.5 Using Pretrained Models: Avg W2V¶
In [83]:
# ''''
# # Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
# def loadGloveModel(gloveFile):
# print ("Loading Glove Model")
# f = open(gloveFile,'r', encoding="utf8")
# model = {}
# for line in tqdm(f):
# splitLine = line.split()
# word = splitLine[0]
# embedding = np.array([float(val) for val in splitLine[1:]])
# model[word] = embedding
# print ("Done.",len(model)," words loaded!")
# return model
# model = loadGloveModel('glove_vectors')
# # ============================
# #Output:
# #Loading Glove Model
# #1917495it [06:32, 4879.69it/s]
# #Done. 1917495 words loaded!
# # ============================
# words = []
# for i in preproced_texts:
# words.extend(i.split(' '))
# for i in preproced_titles:
# words.extend(i.split(' '))
# print("all the words in the coupus", len(words))
# words = set(words)
# print("the unique words in the coupus", len(words))
# inter_words = set(model.keys()).intersection(words)
# print("The number of words that are present in both glove vectors and our coupus", \
# len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
# words_courpus = {}
# words_glove = set(model.keys())
# for i in words:
# if i in words_glove:
# words_courpus[i] = model[i]
# print("word 2 vec length", len(words_courpus))
# # stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# import pickle
# with open('glove_vectors', 'wb') as f:
# pickle.dump(words_courpus, f)
# ''''
In [84]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
import pickle
with open('glove_vectors', 'rb') as f:
model = pickle.load(f,encoding = 'ISO-8859-1')
glove_words = set(model.keys())
In [85]:
# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_vectors.append(vector)
print(len(avg_w2v_vectors))
print(len(avg_w2v_vectors[0]))
1.4.2.6 Using Pretrained Models: AVG W2V on `project_title`
In [86]:
# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_vectors_title.append(vector)
print(len(avg_w2v_vectors_title))
print(len(avg_w2v_vectors_title[0]))
print(type(avg_w2v_vectors_title))
1.4.2.7 Using Pretrained Models: TFIDF weighted W2V¶
In [87]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [88]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_vectors.append(vector)
print(len(tfidf_w2v_vectors))
print(len(tfidf_w2v_vectors[0]))
1.4.2.9 Using Pretrained Models: TFIDF weighted W2V on `project_title`
In [89]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_titles): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_vectors_title.append(vector)
print(len(tfidf_w2v_vectors_title))
print(len(tfidf_w2v_vectors_title[0]))
print(type(tfidf_w2v_vectors_title))
1.4.3 Vectorizing Numerical features¶
In [90]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
price_scalar = StandardScaler()
price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))
In [91]:
price_standardized
Out[91]:
In [92]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
project_scalar = StandardScaler()
project_data['teacher_number_of_previously_posted_projects'] = project_data['teacher_number_of_previously_posted_projects'].astype(float)
project_scalar.fit(project_data['teacher_number_of_previously_posted_projects'].values.reshape(-1,1))
# finding the mean and standard deviation of this data
print(f"Mean : {project_scalar.mean_[0]}, Standard deviation : {np.sqrt(project_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
project_standardized = project_scalar.transform(project_data['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
print(type(project_standardized))
In [93]:
print(len(project_standardized))
project_standardized
Out[93]:
In [94]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287. 73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
quantity_scalar = StandardScaler()
project_data['quantity'] = project_data['quantity'].astype(float)
quantity_scalar.fit(project_data['quantity'].values.reshape(-1,1))
# finding the mean and standard deviation of this data
print(f"Mean : {quantity_scalar.mean_[0]}, Standard deviation : {np.sqrt(quantity_scalar.var_[0])}")
# Now standardize the data with above mean and variance.
quantity_standardized = quantity_scalar.transform(project_data['quantity'].values.reshape(-1, 1))
print(type(quantity_standardized))
In [95]:
print(len(quantity_standardized))
quantity_standardized
Out[95]:
1.4.4 Merging all the above features¶
- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors
In [96]:
tfidf_w2v_vectors_title=np.asarray(tfidf_w2v_vectors_title)
avg_w2v_vectors_title=np.asarray(avg_w2v_vectors_title)
print(categories_one_hot.shape,type(categories_one_hot))
print(sub_categories_one_hot.shape,type(sub_categories_one_hot))
print(state_one_hot.shape,type(state_one_hot))
print(teacher_one_hot.shape,type(teacher_one_hot))
print(grade_one_hot.shape,type(grade_one_hot))
print(title_bow.shape,type(title_bow))
print(title_tfidf.shape,type(title_tfidf))
print(tfidf_w2v_vectors_title.shape,type(tfidf_w2v_vectors_title))
print(avg_w2v_vectors_title.shape,type(avg_w2v_vectors_title))
print(price_standardized.shape,type(price_standardized))
print(project_standardized.shape,type(project_standardized))
In [97]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X = hstack((categories_one_hot, sub_categories_one_hot, text_bow, price_standardized))
X.shape
Out[97]:
In [98]:
print(type(X))
Assignment 2: Apply TSNE
If you are using any code snippet from the internet, you have to provide the reference/citations, as we did in the above cells. Otherwise, it will be treated as plagiarism without citations.
- In the above cells we have plotted and analyzed many features. Please observe the plots and write the observations in markdown cells below every plot.
- EDA: Please complete the analysis of the feature: teacher_number_of_previously_posted_projects
-
- Build the data matrix using these features
- school_state : categorical data (one hot encoding)
- clean_categories : categorical data (one hot encoding)
- clean_subcategories : categorical data (one hot encoding)
- teacher_prefix : categorical data (one hot encoding)
- project_grade_category : categorical data (one hot encoding)
- project_title : text data (BOW, TFIDF, AVG W2V, TFIDF W2V)
- price : numerical
- teacher_number_of_previously_posted_projects : numerical
- Now, plot FOUR t-SNE plots with each of these feature sets.
- categorical, numerical features + project_title(BOW)
- categorical, numerical features + project_title(TFIDF)
- categorical, numerical features + project_title(AVG W2V)
- categorical, numerical features + project_title(TFIDF W2V)
- Concatenate all the features and Apply TNSE on the final data matrix
- Note 1: The TSNE accepts only dense matrices
- Note 2: Consider only 5k to 6k data points to avoid memory issues. If you run into memory error issues, reduce the number of data points but clearly state the number of datat-poins you are using
2.1 TSNE with `BOW` encoding of `project_title` feature
In [99]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
#https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
from sklearn.manifold import TSNE
Y = hstack((categories_one_hot, sub_categories_one_hot, state_one_hot,quantity_standardized, grade_one_hot, teacher_one_hot, price_standardized,project_standardized, title_bow))
print(Y.shape)
print(type(Y))
Y = Y.tocsr()#convert sparse matrix in coordinate format to compressed sparse row matrix
print(type(Y))
Y_new = Y[0:3500,:]
Y_new = Y_new.todense()
print(type(Y_new))
print(Y_new.shape)
Y_new = StandardScaler().fit_transform(Y_new)
In [100]:
labels = project_data["project_is_approved"]
labels_3500 = labels[0: 3500]
model = TSNE(n_components = 2, perplexity = 30.0, random_state = 0)
tsne_data = model.fit_transform(Y_new)
tsne_data = np.vstack((tsne_data.T, labels_3500)).T
tsne_df = pd.DataFrame(data = tsne_data, columns = ("Dim 1","Dim 2","Label"))
print(tsne_df.shape)
In [101]:
sns.FacetGrid(tsne_df, hue = "Label", size = 7).map(plt.scatter, "Dim 1", "Dim 2").add_legend().fig.suptitle("TSNE with `BOW` encoding of `project_title` feature")
plt.show()
2.2 TSNE with `TFIDF` encoding of `project_title` feature
In [102]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
In [103]:
#https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
Y = hstack((categories_one_hot, sub_categories_one_hot, state_one_hot,quantity_standardized, grade_one_hot, teacher_one_hot, price_standardized,project_standardized, title_tfidf))
print(Y.shape)
print(type(Y))
Y = Y.tocsr()#convert sparse matrix in coordinate format to compressed sparse row matrix
print(type(Y))
Y_new = Y[0:3500,:]
Y_new = Y_new.todense()
print(type(Y_new))
print(Y_new.shape)
Y_new = StandardScaler().fit_transform(Y_new)
In [104]:
labels = project_data["project_is_approved"]
labels_3500 = labels[0: 3500]
model = TSNE(n_components = 2, perplexity = 30.0, random_state = 0)
tsne_data = model.fit_transform(Y_new)
tsne_data = np.vstack((tsne_data.T, labels_3500)).T
tsne_df = pd.DataFrame(data = tsne_data, columns = ("Dim 1","Dim 2","Label"))
print(tsne_df.shape)
In [105]:
sns.FacetGrid(tsne_df, hue = "Label", height = 6).map(plt.scatter, "Dim 1", "Dim 2").add_legend().fig.suptitle("TSNE with `TFIDF` encoding of `project_title` feature")
plt.show()
2.3 TSNE with `AVG W2V` encoding of `project_title` feature
In [106]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
In [107]:
#https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
Y = hstack((categories_one_hot, sub_categories_one_hot, state_one_hot,quantity_standardized, grade_one_hot, teacher_one_hot, price_standardized,project_standardized, avg_w2v_vectors_title))
print(Y.shape)
print(type(Y))
Y = Y.tocsr()#convert sparse matrix in coordinate format to compressed sparse row matrix
print(type(Y))
Y_new = Y[0:3500,:]
Y_new = Y_new.todense()
print(type(Y_new))
print(Y_new.shape)
Y_new = StandardScaler().fit_transform(Y_new)
In [108]:
labels = project_data["project_is_approved"]
labels_3500 = labels[0: 3500]
model = TSNE(n_components = 2, perplexity = 0.0, random_state = 0)
tsne_data = model.fit_transform(Y_new)
tsne_data = np.vstack((tsne_data.T, labels_3500)).T
tsne_df = pd.DataFrame(data = tsne_data, columns = ("Dim 1","Dim 2","Label"))
print(tsne_df.shape)
In [109]:
sns.FacetGrid(tsne_df, hue = "Label", height = 6).map(plt.scatter, "Dim 1", "Dim 2").add_legend().fig.suptitle(" TSNE with `AVG W2V` encoding of `project_title` feature")
plt.show()
2.4 TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature
In [110]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
In [111]:
#https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
Y = hstack((categories_one_hot, sub_categories_one_hot, state_one_hot,quantity_standardized, grade_one_hot, teacher_one_hot, price_standardized,project_standardized, avg_w2v_vectors_title))
print(Y.shape)
print(type(Y))
Y = Y.tocsr()#convert sparse matrix in coordinate format to compressed sparse row matrix
print(type(Y))
Y_new = Y[0:3500,:]
Y_new = Y_new.todense()
print(type(Y_new))
#print(issparsematrix(Y_new))
print(Y_new.shape)
Y_new = StandardScaler().fit_transform(Y_new)
In [112]:
labels = project_data["project_is_approved"]
labels_3500 = labels[0: 3500]
model = TSNE(n_components = 2, perplexity = 30.0, random_state = 0)
tsne_data = model.fit_transform(Y_new)
tsne_data = np.vstack((tsne_data.T, labels_3500)).T
tsne_df = pd.DataFrame(data = tsne_data, columns = ("Dim 1","Dim 2","Label"))
print(tsne_df.shape)
In [113]:
sns.FacetGrid(tsne_df, hue = "Label", height = 6).map(plt.scatter, "Dim 1", "Dim 2").add_legend().fig.suptitle(" TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature")
plt.show()
2.4 TSNE with TFIDF Weighted W2V ,project_standardized,title_bow,title_tfidf encoding of project_title feature¶
In [114]:
#https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
Y = hstack((categories_one_hot, sub_categories_one_hot, state_one_hot,quantity_standardized, grade_one_hot, teacher_one_hot, price_standardized,project_standardized,title_bow,title_tfidf,avg_w2v_vectors_title,tfidf_w2v_vectors_title))
print(Y.shape)
print(type(Y))
Y = Y.tocsr()#convert sparse matrix in coordinate format to compressed sparse row matrix
print(type(Y))
Y_new = Y[0:3500,:]
Y_new = Y_new.todense()
print(type(Y_new))
print(Y_new.shape)
Y_new = StandardScaler().fit_transform(Y_new)
In [115]:
labels = project_data["project_is_approved"]
labels_3500 = labels[0: 3500]
model = TSNE(n_components = 2, perplexity = 30.0, random_state = 0)
tsne_data = model.fit_transform(Y_new)
tsne_data = np.vstack((tsne_data.T, labels_3500)).T
tsne_df = pd.DataFrame(data = tsne_data, columns = ("Dim 1","Dim 2","Label"))
print(tsne_df.shape)
In [116]:
sns.FacetGrid(tsne_df, hue = "Label", height = 6).map(plt.scatter, "Dim 1", "Dim 2").add_legend().fig.suptitle(" TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature")
plt.show()
2.5 Summary
After using BoW, TF-IDF, Avg Word2Vec, TF-IDF Weighted Word2Vec the TSNE visualisation does not give us the relevant information .Due to overlapping of points different values of perplextity are also not providing expected results.¶
Due to shortage of RAM, I have loaded glove.42B.300d.txt into glove_vector.I have used 3500 data points while performing TSNE.
In [ ]: